import random

import numpy as np
import pandas as pd
from netZooPy.ligress.bonobo import Bonobo
from bonobo_competitors_scaled import lioness
from bonobo_competitors_scaled import spcc
from bonobo_competitors_scaled import sweet
from bonobo_simulation_module_gene0_unalteredSamples import simulate_bonobo_gene0

random.seed(10)

### Compute MSE for Nsim-many Simulated Data
sim_mean = pd.read_csv("/home/esaha/BONOBO/data/simulation_simple/simulation_mean100.csv")
sim_cov = pd.read_csv("/home/esaha/BONOBO/data/simulation_simple/simulation_covariance100.csv", sep = " ")

mix_prop = 0.5

Nsim = 10
nsample = 100
ngene = 100
mse_bonobo = []
mse_bonobo_sparse = []
mse_lioness = []
mse_spcc = []
mse_sweet = []
mse_sparse_sweet = []
for i in range(Nsim):
  mse_b, mse_bs, mse_l, mse_s, mse_sw, mse_ssw = simulate_bonobo_gene0(sim_mean, sim_cov, ngene, nsample, mix_prop, nsample_ind = 100)
  mse_b = sum(mse_b)/len(mse_b)
  mse_bs = sum(mse_bs)/len(mse_bs)
  mse_l = sum(mse_l)/len(mse_l)
  mse_s = sum(mse_s)/len(mse_s)
  mse_sw = sum(mse_sw)/len(mse_sw)
  mse_ssw = sum(mse_ssw)/len(mse_ssw)
  mse_bonobo.append(mse_b)
  mse_bonobo_sparse.append(mse_bs)
  mse_lioness.append(mse_l)
  mse_spcc.append(mse_s)
  mse_sweet.append(mse_sw)
  mse_sparse_sweet.append(mse_ssw)
  

# Combine the lists using pandas
results = pd.DataFrame({
    "mse_bonobo": mse_bonobo,
    "mse_bonobo_sparse": mse_bonobo_sparse,
    "mse_lioness": mse_lioness,
    "mse_spcc": mse_spcc,
    "mse_sweet": mse_sweet,
    "mse_sparse_sweet": mse_sparse_sweet
})

# Save the combined DataFrame to a text file
results.to_csv("bonobo_simulation_results_gene0_unalteredSamples_prop" + str(mix_prop) + ".txt", sep="\t", index=False)


results.mean(axis=0)


